How to start using AI in your company

Many organizations feel pressure to adopt AI, but most aren’t sure how it fits into their daily operations. Leaders hear promises of automation and transformation, while teams feel overwhelmed by jargon and unsure whether their data is even ready.

The truth is that implementing AI doesn’t require huge budgets or futuristic systems. What it needs is a clear understanding of the data a company already has and a realistic view of where AI can make work easier, faster, or more consistent.

This article shares a practical way for businesses to get started without overhauling everything at once.

The Reality Most Organizations Face

Across industries, a few challenges show up again and again:

• Companies feel pushed to “do AI” because competitors claim they are or the board/ELT requests it.

• Internal teams don’t have the capability to run AI projects

• Companies don’t have the tech foundations required

• Too much noise and overpromising solutions..

All of this leads to hesitation. Teams want to make progress but don’t know where to begin in a way that feels realistic.

A Practical Way Forward: Start Small and Start Smart

AI works best when it grows out of a strong understanding of your own data. That means knowing what information you have, how clean it is, and where automation can actually help.

Sisifo’s recommended roadmap looks like this:

1. Readiness and Alignment

Before building anything, organizations benefit from a clear view of:

• Their existing data sources, such as ERP, CRM, finance, and sales systems.

• The reliability and accessibility of that data.

• The specific problems AI can help solve, such as report automation, document classification, customer segmentation, recommendation logic, or internal search.

This step builds realistic expectations and helps everyone align on where AI can make an immediate difference.

2. Pilot and Proof of Value

Instead of long, expensive pilots, companies can start with small, measurable prototypes.

Modern APIs like OpenAI, Claude, or Amazon Bedrock make it possible to build quick experiments that connect directly to existing data.

Common early use cases include:

• Automated document reading (invoices, contracts, purchase orders, bills of lading, etc)

• AI-assisted report creation

• Automated client engagement messages

• Process automation with agents

• Internal natural-language search

These prototypes typically launch in a matter of weeks and give teams something they can actually use, test, and learn from.

Make sure to include your operating teams early in the process, so they own the scale up step confidently.

3. Scale

Once a pilot proves valuable, it can be integrated into existing dashboards, workflows, or pipelines.

This stage usually focuses on:

• Adding governance and clear ownership

• Managing security and access

• Keeping costs predictable

• Expanding the solution only when there is a strong return on investment

AI then becomes a dependable part of operations, growing steadily instead of all at once.

Why Affordable AI Is Possible Today

The cost of building useful AI tools has dropped significantly thanks to:

• Modern APIs that reduce the need for custom modeling

• Cloud orchestration tools that stay inexpensive, even at scale

• Serverless operations that keep infrastructure costs low

• The ability to test before making major commitments

Getting started no longer requires a large team or a large budget. It simply requires thoughtful scoping and a clear connection to real business needs.

Benefits of starting small

Organizations that take a grounded approach to AI usually see:

• Clear expectations about where AI will help and where it won’t (fail or succeed fast and learn mentality)

• Teams feel comfortable using the tools from the beginning as they help design it

• A steady sense of progress without unnecessary spending

The goal is not to chase trends. It is to make meaningful improvements that last.

Closing Thought

AI doesn’t need to be overwhelming or abstract. When there is thoughtful planning, it becomes a practical tool that can be implemented quickly for specific use cases.

The companies seeing the strongest results today are not the ones spending the most money. They are the ones implementing AI with targeted use cases and a realistic plan.